Neurocognition and adaptive functioning in a genetic high risk model of schizophrenia
Bibliographic record
Abstract
BACKGROUND: Identifying factors that influence the functional outcome is an important goal in schizophrenia research. The 22q11.2 deletion syndrome (22q11DS) is a unique genetic model with high risk (20-25%) for schizophrenia. This study aimed to identify potentially targetable domains of neurocognitive functioning associated with functional outcome in adults with 22q11DS. METHODS: We used comprehensive neurocognitive test data available for 99 adults with 22q11DS (n = 43 with schizophrenia) and principal component analysis to derive four domains of neurocognition (Verbal Memory, Visual and Logical Memory, Motor Performance, and Executive Performance). We then investigated the association of these neurocognitive domains with adaptive functioning using Vineland Adaptive Behavior Scales data and a linear regression model that accounted for the effects of schizophrenia status and overall intellectual level. RESULTS: The regression model explained 46.8% of the variance in functional outcome (p < 0.0001). Executive Performance was significantly associated with functional outcome (p = 0.048). Age and schizophrenia were also significant factors. The effects of Executive Performance on functioning did not significantly differ between those with and without psychotic illness. CONCLUSION: The findings provide the impetus for further studies to examine the potential of directed (early) interventions targeting Executive Performance to improve long-term adaptive functional outcome in individuals with, or at high risk for, schizophrenia. Moreover, the neurocognitive test profiles may benefit caregivers and clinicians by providing insight into the relative strengths and weaknesses of individuals with 22q11DS, with and without psychotic illness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".